Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	14.0	1.025280
daal4py_KNeighborsClassifier	0.0	3.0	21.205253
KNeighborsClassifier_kd_tree	0.0	6.0	38.832610
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	43.740270
KMeans_tall	0.0	1.0	44.209824
daal4py_KMeans_tall	0.0	1.0	17.469926
KMeans_short	0.0	0.0	19.505816
daal4py_KMeans_short	0.0	0.0	8.757342
LogisticRegression	0.0	1.0	2.355693
daal4py_LogisticRegression	0.0	0.0	55.327638
Ridge	0.0	0.0	0.974624
daal4py_Ridge	0.0	0.0	0.645498
total	0.0	31.0	14.126336

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.135	0.001	1000000	1000000	100	brute	-1	1	NaN	NaN	0.492	0.007	0.273	0.000	See
1	KNeighborsClassifier	predict	0.174	0.012	1000000	1	100	brute	-1	1	0.0	1.0	0.087	0.002	2.000	0.005	See
2	KNeighborsClassifier	predict	25.404	0.008	1000000	1000	100	brute	-1	1	0.0	1.0	2.003	0.044	12.684	0.000	See
3	KNeighborsClassifier	fit	0.134	0.003	1000000	1000000	100	brute	-1	5	NaN	NaN	0.489	0.005	0.275	0.001	See
4	KNeighborsClassifier	predict	0.170	0.016	1000000	1	100	brute	-1	5	1.0	1.0	0.086	0.002	1.980	0.010	See
5	KNeighborsClassifier	predict	36.615	0.000	1000000	1000	100	brute	-1	5	1.0	1.0	1.988	0.052	18.419	0.001	See
6	KNeighborsClassifier	fit	0.122	0.001	1000000	1000000	100	brute	-1	100	NaN	NaN	0.489	0.002	0.249	0.000	See
7	KNeighborsClassifier	predict	0.177	0.018	1000000	1	100	brute	-1	100	1.0	1.0	0.085	0.001	2.079	0.010	See
8	KNeighborsClassifier	predict	36.669	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	2.024	0.018	18.119	0.000	See
9	KNeighborsClassifier	fit	0.123	0.001	1000000	1000000	100	brute	1	1	NaN	NaN	0.490	0.004	0.250	0.000	See
10	KNeighborsClassifier	predict	0.181	0.002	1000000	1	100	brute	1	1	0.0	1.0	0.084	0.001	2.141	0.000	See
11	KNeighborsClassifier	predict	13.474	0.046	1000000	1000	100	brute	1	1	0.0	1.0	1.941	0.011	6.941	0.000	See
12	KNeighborsClassifier	fit	0.123	0.001	1000000	1000000	100	brute	1	5	NaN	NaN	0.488	0.002	0.253	0.000	See
13	KNeighborsClassifier	predict	0.190	0.002	1000000	1	100	brute	1	5	1.0	1.0	0.085	0.000	2.238	0.000	See
14	KNeighborsClassifier	predict	25.727	0.104	1000000	1000	100	brute	1	5	1.0	1.0	1.951	0.015	13.185	0.000	See
15	KNeighborsClassifier	fit	0.135	0.003	1000000	1000000	100	brute	1	100	NaN	NaN	0.482	0.004	0.280	0.001	See
16	KNeighborsClassifier	predict	0.189	0.002	1000000	1	100	brute	1	100	1.0	1.0	0.084	0.000	2.247	0.000	See
17	KNeighborsClassifier	predict	25.348	0.014	1000000	1000	100	brute	1	100	1.0	1.0	2.021	0.022	12.540	0.000	See
18	KNeighborsClassifier	fit	0.060	0.001	1000000	1000000	2	brute	-1	1	NaN	NaN	0.107	0.003	0.556	0.001	See
19	KNeighborsClassifier	predict	0.023	0.009	1000000	1	2	brute	-1	1	1.0	0.0	0.006	0.000	3.886	0.161	See
20	KNeighborsClassifier	predict	21.676	0.056	1000000	1000	2	brute	-1	1	1.0	0.0	0.303	0.004	71.500	0.000	See
21	KNeighborsClassifier	fit	0.059	0.001	1000000	1000000	2	brute	-1	5	NaN	NaN	0.106	0.003	0.561	0.001	See
22	KNeighborsClassifier	predict	0.033	0.004	1000000	1	2	brute	-1	5	1.0	0.0	0.006	0.001	5.421	0.040	See
23	KNeighborsClassifier	predict	32.751	0.000	1000000	1000	2	brute	-1	5	1.0	0.0	0.304	0.003	107.805	0.000	See
24	KNeighborsClassifier	fit	0.058	0.001	1000000	1000000	2	brute	-1	100	NaN	NaN	0.106	0.001	0.547	0.000	See
25	KNeighborsClassifier	predict	0.032	0.001	1000000	1	2	brute	-1	100	1.0	0.0	0.007	0.001	4.814	0.026	See
26	KNeighborsClassifier	predict	32.720	0.000	1000000	1000	2	brute	-1	100	1.0	0.0	0.362	0.007	90.354	0.000	See
27	KNeighborsClassifier	fit	0.061	0.001	1000000	1000000	2	brute	1	1	NaN	NaN	0.106	0.002	0.574	0.001	See
28	KNeighborsClassifier	predict	0.015	0.001	1000000	1	2	brute	1	1	1.0	0.0	0.006	0.000	2.608	0.007	See
29	KNeighborsClassifier	predict	10.435	0.014	1000000	1000	2	brute	1	1	1.0	0.0	0.303	0.003	34.459	0.000	See
30	KNeighborsClassifier	fit	0.059	0.001	1000000	1000000	2	brute	1	5	NaN	NaN	0.105	0.001	0.562	0.001	See
31	KNeighborsClassifier	predict	0.027	0.001	1000000	1	2	brute	1	5	1.0	0.0	0.006	0.001	4.355	0.055	See
32	KNeighborsClassifier	predict	21.485	0.013	1000000	1000	2	brute	1	5	1.0	0.0	0.303	0.005	70.886	0.000	See
33	KNeighborsClassifier	fit	0.060	0.001	1000000	1000000	2	brute	1	100	NaN	NaN	0.106	0.004	0.566	0.002	See
34	KNeighborsClassifier	predict	0.027	0.001	1000000	1	2	brute	1	100	1.0	0.0	0.006	0.001	4.534	0.009	See
35	KNeighborsClassifier	predict	21.544	0.030	1000000	1000	2	brute	1	100	1.0	0.0	0.371	0.011	58.028	0.001	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.027	0.029	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.727	0.018	4.165	0.001	See
1	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	13.207	0.205	See
2	KNeighborsClassifier_kd_tree	predict	0.448	0.008	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.104	0.003	4.327	0.001	See
3	KNeighborsClassifier_kd_tree	fit	3.032	0.034	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.744	0.010	4.074	0.000	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.000	0.000	6.459	0.240	See
5	KNeighborsClassifier_kd_tree	predict	0.849	0.015	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.195	0.006	4.359	0.001	See
6	KNeighborsClassifier_kd_tree	fit	3.086	0.037	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.759	0.018	4.066	0.001	See
7	KNeighborsClassifier_kd_tree	predict	0.005	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.001	0.000	6.493	0.147	See
8	KNeighborsClassifier_kd_tree	predict	2.558	0.027	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.559	0.004	4.577	0.000	See
9	KNeighborsClassifier_kd_tree	fit	3.117	0.049	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.766	0.014	4.069	0.001	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	4.491	0.264	See
11	KNeighborsClassifier_kd_tree	predict	0.737	0.015	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.102	0.001	7.229	0.001	See
12	KNeighborsClassifier_kd_tree	fit	3.064	0.064	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.740	0.014	4.141	0.001	See
13	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	1.0	1.0	0.000	0.000	3.220	0.236	See
14	KNeighborsClassifier_kd_tree	predict	1.392	0.012	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.190	0.003	7.313	0.000	See
15	KNeighborsClassifier_kd_tree	fit	3.031	0.036	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.775	0.021	3.912	0.001	See
16	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.001	0.000	3.722	0.248	See
17	KNeighborsClassifier_kd_tree	predict	4.645	0.024	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.559	0.003	8.310	0.000	See
18	KNeighborsClassifier_kd_tree	fit	1.275	0.009	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.498	0.010	2.562	0.000	See
19	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	24.463	0.643	See
20	KNeighborsClassifier_kd_tree	predict	0.028	0.001	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	26.619	0.045	See
21	KNeighborsClassifier_kd_tree	fit	1.246	0.007	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.496	0.008	2.515	0.000	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	13.845	0.291	See
23	KNeighborsClassifier_kd_tree	predict	0.033	0.003	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.000	28.250	0.074	See
24	KNeighborsClassifier_kd_tree	fit	1.267	0.009	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.494	0.010	2.566	0.000	See
25	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	14.183	0.466	See
26	KNeighborsClassifier_kd_tree	predict	0.050	0.001	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.007	0.001	6.854	0.014	See
27	KNeighborsClassifier_kd_tree	fit	1.268	0.009	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.497	0.009	2.553	0.000	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	5.423	0.466	See
29	KNeighborsClassifier_kd_tree	predict	0.025	0.001	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	24.002	0.110	See
30	KNeighborsClassifier_kd_tree	fit	1.262	0.010	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.490	0.008	2.576	0.000	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	4.952	0.514	See
32	KNeighborsClassifier_kd_tree	predict	0.028	0.001	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.000	24.224	0.098	See
33	KNeighborsClassifier_kd_tree	fit	1.266	0.009	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.487	0.006	2.600	0.000	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	4.956	0.607	See
35	KNeighborsClassifier_kd_tree	predict	0.054	0.002	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.007	0.001	7.576	0.028	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.637	0.013	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.468	0.031	1.362	0.005	See
1	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.361	0.607	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.602	0.332	See
3	KMeans_tall	fit	0.561	0.009	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.434	0.014	1.291	0.001	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.287	0.531	See
5	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.048	0.546	See
6	KMeans_tall	fit	6.573	0.065	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	3.079	0.016	2.135	0.000	See
7	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.578	0.414	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.824	0.198	See
9	KMeans_tall	fit	5.925	0.105	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	2.927	0.038	2.025	0.000	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.917	0.519	See
11	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.066	0.184	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.326	0.012	10000	10000	2	full	k-means++	30	300	1	0.0	30.0	NaN	30.0	NaN	0.116	0.003	2.818	0.002	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.021	0.506	See
2	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.107	0.130	See
3	KMeans_short	fit	0.128	0.003	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.047	0.001	2.729	0.002	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.982	0.473	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.221	0.145	See
6	KMeans_short	fit	0.844	0.038	10000	10000	100	full	k-means++	30	300	1	0.0	19.0	NaN	22.0	NaN	0.388	0.022	2.176	0.005	See
7	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.098	0.324	See
8	KMeans_short	predict	0.006	0.002	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	3.711	0.163	See
9	KMeans_short	fit	0.258	0.036	10000	10000	100	full	random	30	300	1	0.0	23.0	NaN	20.0	NaN	0.183	0.026	1.408	0.039	See
10	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.894	0.286	See
11	KMeans_short	predict	0.004	0.003	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	2.825	0.579	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.382	0.014	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	11.396	0.031	0.999	0.000	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.323	1.277	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	1.064	0.514	See
3	LogisticRegression	fit	0.740	0.013	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	0.757	0.007	0.978	0.000	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.000	0.133	0.407	See
5	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.003	0.000	0.544	0.034	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	0.037	0.001	1000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.021	0.003	1.782	0.021	See
1	Ridge	predict	0.000	0.000	1000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.669	0.870	See
2	Ridge	predict	0.000	0.000	1000	100	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.832	0.547	See
3	Ridge	fit	0.008	0.001	10000	10000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.005	0.002	1.722	0.156	See
4	Ridge	predict	0.000	0.000	10000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.775	0.965	See
5	Ridge	predict	0.000	0.000	10000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.413	0.371	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}